Regression models have been used to produce prediction rules for predicting whether an individual has a particular attribute, such as a sleeping disorder. Such regression models often use physical features and clinical traits indicative of a high probability of the attribute. However, the predictive rules produced by such regression models often lack specificity, and offer only a dichotomous output based on a predetermined cutoff value, particularly with respect to the apnea/hypopnea index ("AHI"), which is an index related to obstructive sleep apnea ("OSA"). Furthermore, to compound the deficiencies of such prediction rules, the predetermined cutoff value associated with a particular model is often selected arbitrarily, and furthermore, varies from model to model. Moreover, the statistical methods used to derive these models may not fully take into account nonlinear attributes of complex processes.
OSA has been recognized increasingly as an important public health problem with potentially serious cardiovascular and psychomotor morbidity, and possibly excessive mortality. An increased awareness of the risks associated with OSA in recent years has led to an increase in the number of referrals to specialists and sleep laboratories. Overnight polysomnography ("OPG") is the standard reference test for diagnosing OSA. However, OPG is an expensive, labor-intensive, and time consuming procedure. As a result, there is a need for a practical and less work-intensive screening test to allow physicians to estimate whether patients have OSA.